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• W2076805575 abstract "RATIONALE: Food allergy diagnosis is best done by oral food challenge (OFC). Many units do not do OFC routinely, relying on skin test or specific IgE decision points. Other factors may also be influential. We examined the predictive value of a novel algorithm, designed to predict a post challenge result from pre-challenge data, on children with and without a history of reactivity to the food tested.METHODS: The algorithm was retrospectively validated using data from 340 children undergoing OFC and prospectively validated on 60 further children at the same clinic. We split the retrospective generating data set into Group A (history of reactivity) and Group B (no history). ANOVA was used to examine differences. Receiver Operating Characteristic (ROC) evaluated sensitivity and specificity.RESULTS: 44% of challenges were positive, 56% were negative. Group B patients (no history; 30%) had higher levels of IgE and SPT but had a significantly lower level of positive results in OFC (36%; p<0.05). The algorithm was prospectively found to be 90% accurate in Group A, 82% accurate in Group B (p<0.05). Group differences were found in: allergen types; age; SPT; IgE and challenge results (<0.05). The algorithm has high positive and negative predictive value. Area under the curve (AUC) 0.898 p=0.01CONCLUSIONS: This prospectively validated algorithm can predict OFC outcome with a high degree of accuracy, from a routinely available pre challenge set of variables. The model accommodates missing data, all levels SPT/IgE, food types, age. It can be easily used by health professionals at all levels of practice. RATIONALE: Food allergy diagnosis is best done by oral food challenge (OFC). Many units do not do OFC routinely, relying on skin test or specific IgE decision points. Other factors may also be influential. We examined the predictive value of a novel algorithm, designed to predict a post challenge result from pre-challenge data, on children with and without a history of reactivity to the food tested. METHODS: The algorithm was retrospectively validated using data from 340 children undergoing OFC and prospectively validated on 60 further children at the same clinic. We split the retrospective generating data set into Group A (history of reactivity) and Group B (no history). ANOVA was used to examine differences. Receiver Operating Characteristic (ROC) evaluated sensitivity and specificity. RESULTS: 44% of challenges were positive, 56% were negative. Group B patients (no history; 30%) had higher levels of IgE and SPT but had a significantly lower level of positive results in OFC (36%; p<0.05). The algorithm was prospectively found to be 90% accurate in Group A, 82% accurate in Group B (p<0.05). Group differences were found in: allergen types; age; SPT; IgE and challenge results (<0.05). The algorithm has high positive and negative predictive value. Area under the curve (AUC) 0.898 p=0.01 CONCLUSIONS: This prospectively validated algorithm can predict OFC outcome with a high degree of accuracy, from a routinely available pre challenge set of variables. The model accommodates missing data, all levels SPT/IgE, food types, age. It can be easily used by health professionals at all levels of practice." @default.
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• W2076805575 date "2010-02-01" @default.
• W2076805575 modified "2023-09-27" @default.
• W2076805575 title "Take a History! Predicting the Outcome of Oral Food Challenge Using More Than Just SPT and Specific IgE Levels" @default.
• W2076805575 doi "https://doi.org/10.1016/j.jaci.2009.12.326" @default.
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